倾斜摄影测量技术提取落叶松人工林地形信息
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摘要
【目的】林下地形是提取单木树高、冠幅等森林参数的必备条件,但由于林区地形起伏较大,传统的测量手段难以获取大范围高精度的森林地区数字地形模型(DTM)。近年来,倾斜摄影测量克服了传统测量技术的缺点,成为获取三维地理信息的新型手段。本文使用无人机倾斜摄影测量技术提取落叶松林下地形,并评测精度与适用性,为后续基于倾斜摄影测量技术提取单木参数的研究提供参考。【方法】选择内蒙古旺业甸林场内山区典型落叶松幼龄林、中龄林和成熟林林分在落叶季进行无人机飞行,采用Context Capture软件对获取的落叶季倾斜像片进行三维重建,生成林区点云。使用布料模拟滤波、加权线性最小二乘、渐进不规则三角网加密法、渐进形态学滤波算法从点云中提取地面点,并采用3种插值方法插值地面点生成测区完整地形。使用激光雷达DTM作为验证数据评价精度。【结果】不同算法的地形提取精度与郁闭度相关。在幼龄林区域和中龄林区域,布料模拟滤波提取地面点的精度最高,决定系数(R~2)均达到0.999,均方根误差(RMSE)分别为1.61 m和0.47 m;在成熟林区域,渐进三角网滤波效果最好,R~2为0.999,RMSE为0.39 m。在不同郁闭度林分选择最优滤波算法基础上,比较不同插值方法生成的数字地形模型(DTM)精度,结果表明:在幼龄林和中龄林,布料模拟滤波点云后经不规则三角网(TIN)插值得到的DTM精度最高,RMSE分别为1.58 m和0.44 m;成熟林分渐进不规则三角网加密滤波后地面点经克里金(Kriging)法插值得到的DTM精度最高,RMSE为0.31 m。【结论】实验证明,倾斜摄影测量技术可用于落叶松林分地形提取。
[Objective] The underforest terrain is a necessary condition for extracting forest parameters such as individual tree height and crown width. However, due to the large terrain fluctuation of the forest area, it is difficult to obtain a large-scale and high-precision digital terrain model(DTM) of forest area by traditional measurement method. Oblique photogrammetry overcomes the shortcomings of traditional measurement technology and becomes a new method to obtain three-dimensional geographic information. In this paper,UAV oblique photogrammetry technology was used to extract the topography of larch forest, and its accuracy and applicability were evaluated. It provides a reference for subsequent research on extracting individual tree parameters based on oblique photogrammetry technology. [Method] The typical young, middle-aged and mature Larix forests in the mountainous area of Wangyedian Forest Farm in InnerMongolia, northern China were selected for UAV flight in the deciduous season. The oblique images of the deciduous season were reconstructed by Context Capture software to generate point clouds in the forest area.Ground points were extracted from point clouds by cloth simulation filtering(CSF), weighted linear least squares(WLS), progressive irregular triangular network filtering(PTIN) and progressive morphological filtering(PMF), and three interpolation methods were used to interpolate ground points to generate complete topography in the survey area. DTM generated from Li DAR data was used to evaluate the extraction accuracy with validated data. [Result] The results showed that the accuracy of terrain extraction by different algorithms was related to canopy density. In young forest and middle-aged forest area, cloth simulation filter(CSF) had the highest accuracy in extracting ground points from photogrammetric point clouds, with the determination coefficient(R~2) reaching 0.999 and the root mean square error(RMSE)reaching 1.61 m and 0.47 m, respectively. In mature forest area, progressive triangulated irregular network(PTIN) had the best effect, with R~2 0.999 and RMSE 0.39 m. After selecting the optimal filtering algorithm for different canopy density forest stands, the DTM accuracy of different interpolation methods was compared. The results showed that in young and middle-aged forests, the DTMs both generated by the point clouds of cloth simulation filtering(CSF) and triangulated irregular network(TIN) interpolation had the highest precision, RMSE was 1.58 m and 0.44 m, respectively. In mature forest, the DTM generated by the point clouds of progressive triangulated irregular network flitering(PTIN) and kriging(KRG) interpolation had the highest precision, RMSE was 0.31 m. [Conclusion] Research has shown that oblique photogrammetry can be used for topographic extraction of larch forests.
[Objective] The underforest terrain is a necessary condition for extracting forest parameters such as individual tree height and crown width. However, due to the large terrain fluctuation of the forest area, it is difficult to obtain a large-scale and high-precision digital terrain model(DTM) of forest area by traditional measurement method. Oblique photogrammetry overcomes the shortcomings of traditional measurement technology and becomes a new method to obtain three-dimensional geographic information. In this paper,UAV oblique photogrammetry technology was used to extract the topography of larch forest, and its accuracy and applicability were evaluated. It provides a reference for subsequent research on extracting individual tree parameters based on oblique photogrammetry technology. [Method] The typical young, middle-aged and mature Larix forests in the mountainous area of Wangyedian Forest Farm in InnerMongolia, northern China were selected for UAV flight in the deciduous season. The oblique images of the deciduous season were reconstructed by Context Capture software to generate point clouds in the forest area.Ground points were extracted from point clouds by cloth simulation filtering(CSF), weighted linear least squares(WLS), progressive irregular triangular network filtering(PTIN) and progressive morphological filtering(PMF), and three interpolation methods were used to interpolate ground points to generate complete topography in the survey area. DTM generated from Li DAR data was used to evaluate the extraction accuracy with validated data. [Result] The results showed that the accuracy of terrain extraction by different algorithms was related to canopy density. In young forest and middle-aged forest area, cloth simulation filter(CSF) had the highest accuracy in extracting ground points from photogrammetric point clouds, with the determination coefficient(R~2) reaching 0.999 and the root mean square error(RMSE)reaching 1.61 m and 0.47 m, respectively. In mature forest area, progressive triangulated irregular network(PTIN) had the best effect, with R~2 0.999 and RMSE 0.39 m. After selecting the optimal filtering algorithm for different canopy density forest stands, the DTM accuracy of different interpolation methods was compared. The results showed that in young and middle-aged forests, the DTMs both generated by the point clouds of cloth simulation filtering(CSF) and triangulated irregular network(TIN) interpolation had the highest precision, RMSE was 1.58 m and 0.44 m, respectively. In mature forest, the DTM generated by the point clouds of progressive triangulated irregular network flitering(PTIN) and kriging(KRG) interpolation had the highest precision, RMSE was 0.31 m. [Conclusion] Research has shown that oblique photogrammetry can be used for topographic extraction of larch forests.
引文
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[2]Keith H, Mackey B G, Lindenmayer D B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbondense forests[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(28):11635-11640.
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[4]李欢,李明泽,范文义,等.基于机载激光雷达的林隙结构参数提取[J].林业科学, 2018, 54(10):98-107.Li H, Li M Z, Fan W Y, et al. Canopy gap structure parameters extraction based on light detection and ranging(LiDAR)[J].Scientia Silvae Sinicae, 2018, 54(10):98-107.
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[6]Ota T, Ogawa M, Shimizu K, et al. Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest[J]. Forests, 2015, 6(11):3882-3898.
[7]李增元,刘清旺,庞勇.激光雷达森林参数反演研究进展[J].遥感学报, 2016, 20(5):1138-1150.Li Z Y, Liu Q W, Pang Y. Review on forest parameters inversion using LiDAR[J]. Journal of Remote Sensing, 2016, 20(5):1138-1150.
[8]刘清旺,李世明,李增元,等.无人机激光雷达与摄影测量林业应用研究进展[J].林业科学, 2017, 53(7):134-148.Liu Q W, Li S M, Li Z Y, et al. Review on the applications of UAV-Based LiDAR and photogrammetry in forestry[J]. Scientia Silvae Sinicae, 2017, 53(7):134-148.
[9]Dandois J P, Ellis E C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision[J].Remote Sensing of Environment, 2013, 136:259-276.
[10]徐思奇,黄先锋,张帆,等.倾斜摄影测量技术在大比例尺地形图测绘中的应用[J].测绘通报, 2018(2):111-115.Xu S Q, Huang X F, Zhang F, et al. Oblique photogrammetric technique applied in surveying and mapping large-scale topographic map[J]. Bulletin of Surveying and Mapping, 2018(2):111-115.
[11]周晓敏,孟晓林,张雪萍,等.倾斜摄影测量的城市真三维模型构建方法[J].测绘科学, 2016, 41(9):159-163.Zhou X M, Meng X L, Zhang X P, et al. A method for urban real3D model building based on oblique photogrammetry[J]. Science of Surveying and Mapping, 2016, 41(9):159-163.
[12]杨国东,王民水.倾斜摄影测量技术应用及展望[J].测绘与空间地理信息, 2016, 39(1):13-15, 18.Yang G D, Wang M S. The tilt photographic measuration technique and expectation[J]. Geomatics&Spatial Information Technology, 2016, 39(1):13-15, 18.
[13]魏占玉, Ramon A,何宏林,等.基于SfM方法的高密度点云数据生成及精度分析[J].地震地质, 2015, 37(2):636-648.Wei Z Y, Ramon A, Heng H L, et al. Accuracy analysis of terrain point cloud acquired by “structure from motion” using aerial photos[J]. Seismology and Geology, 2015, 37(2):636-648.
[14]Lin J, Wang M, Ma M, et al. Aboveground tree biomass estimation of sparse subalpine coniferous forest with UAV oblique photography[J]. Remote Sensing, 2018, 10(11):1849-1868.
[15]陈崇成,李旭,黄洪宇.基于无人机影像匹配点云的苗圃单木冠层三维分割[J].农业机械学报, 2018, 49(2):149-155, 206.Chen C C, Li X, Huang H Y. 3D Segmentation of individual tree canopy in forest nursery based on drone image-matching point cloud[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(2):149-155, 206.
[16]Wallace L, Lucieer A, Malenovsky Z, et al. Assessment of forest structure using two UAV techniques:a comparison of airborne laser scanning and structure from motion(SfM)point clouds[J].Forests, 2016, 7(3):62-78.
[17]Sithole G, Vosselman G. Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2004, 59(1-2):85-101.
[18]Silva C A, Klauberg C, Hentz M K, et al. Comparing the performance of ground filtering algorithms for terrain modeling in a forest environment using airborne LiDAR data[J/OL]. Floresta e Ambiente, 2018, 25(2):e20160150[2018-08-12]. http://dx.doi.org/10.1590/2179-8087.015016.
[19]汪垚,张志玉,倪文俭,等.基于机载LiDAR数据的林下地形提取算法比较与组合分析[J].北京林业大学学报, 2017, 39(12):25-35.Wang Y, Zhang Z Y, Ni W J, et al. Comparison of filter algorithms and combination analysis for DEM extracting based on airborne laser scanning point clouds[J]. Journal of Beijing Forestry University, 2017, 39(12):25-35.
[20]胡永杰,程朋根,陈晓勇,等.机载激光雷达点云滤波算法分析与比较[J].测绘科学技术学报, 2015, 32(1):72-77.Hu Y J, Cheng P G, Chen X Y, et al. The analysis and comparison of airborne LiDAR point cloud filter algorithms[J]. Journal of Geomatics Science and Technology, 2015, 32(1):72-77.
[21]White J C, Tompalski P, Coops N C, et al. Comparison of airborne laser scanning and digital stereo imagery for characterizing forest canopy gaps in coastal temperate rainforests[J]. Remote Sensing of Environment, 2018, 208:1-14.
[22]Zhang W, Qi J, Wan P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing,2016,8(6):501-523.
[23]Axelsson P.DEM generation from laser scanner data using adaptive TIN models[J].International Archives of Photogrammetry and Remote Sensing,2000,33(B4/1,PART4):111-118.
[24]Vosselman G.Slope based filtering of laser altimetry data[J].International Archives of Photogrammetry and Remote Sensing,2000,33(B3/2,PART3):935-942.
[25]Zhang K Q,Chen S Q.A progressive morphological filter for removing nonground measurements from airborne LIDAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2003,4(41):872-882.
[26]段祝庚,肖化顺,袁伟湘.基于离散点云数据的森林冠层高度模型插值方法[J].林业科学,2016,52(9):86-94.Duan Z G,Xiao H S,Yuan W X.Comparison of interpolation methods of forest canopy height model using discrete point cloud data[J].Scientia Silvae Sinicae,2016,52(9):86-94.
[27]王彬,孙虎,徐倩,等.基于无人3D摄影技术的雪松(Cedrus deodara)群落高度测定[J].生态学报,2018,38(10):3524-3533.Wang B,Sun H,Xu Q,et al.Height measurement of a cedar(Cedrus deodara)community based on unmanned aerial vehicles(UAV)3D photogrammetry technology[J].Acta Ecologica Sinica,2018,38(10):3524-3533.
[28]Ni W,Sun G,Pang Y,et al.Mapping three-dimensional structures of forest canopy using UAV stereo imagery:evaluating impacts of forward overlaps and image resolutions with Li DAR data as reference[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(10):3578-3589.
[2]Keith H, Mackey B G, Lindenmayer D B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbondense forests[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(28):11635-11640.
[3]冯静静,张晓丽,刘会玲.基于灰度梯度图像分割的单木树冠提取研究[J].北京林业大学学报, 2017, 39(3):16-23.Feng J J, Zhang X L, Liu H L. Single tree crown extraction based on gray gradient image segmentation[J]. Journal of Beijing Forestry University, 2017, 39(3):16-23.
[4]李欢,李明泽,范文义,等.基于机载激光雷达的林隙结构参数提取[J].林业科学, 2018, 54(10):98-107.Li H, Li M Z, Fan W Y, et al. Canopy gap structure parameters extraction based on light detection and ranging(LiDAR)[J].Scientia Silvae Sinicae, 2018, 54(10):98-107.
[5]Barnes C, Balzter H, Barrett K, et al. Individual tree crown delineation from airborne laser scanning for diseased larch forest stands[J]. Remote Sensing, 2017, 9(3):231-251.
[6]Ota T, Ogawa M, Shimizu K, et al. Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest[J]. Forests, 2015, 6(11):3882-3898.
[7]李增元,刘清旺,庞勇.激光雷达森林参数反演研究进展[J].遥感学报, 2016, 20(5):1138-1150.Li Z Y, Liu Q W, Pang Y. Review on forest parameters inversion using LiDAR[J]. Journal of Remote Sensing, 2016, 20(5):1138-1150.
[8]刘清旺,李世明,李增元,等.无人机激光雷达与摄影测量林业应用研究进展[J].林业科学, 2017, 53(7):134-148.Liu Q W, Li S M, Li Z Y, et al. Review on the applications of UAV-Based LiDAR and photogrammetry in forestry[J]. Scientia Silvae Sinicae, 2017, 53(7):134-148.
[9]Dandois J P, Ellis E C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision[J].Remote Sensing of Environment, 2013, 136:259-276.
[10]徐思奇,黄先锋,张帆,等.倾斜摄影测量技术在大比例尺地形图测绘中的应用[J].测绘通报, 2018(2):111-115.Xu S Q, Huang X F, Zhang F, et al. Oblique photogrammetric technique applied in surveying and mapping large-scale topographic map[J]. Bulletin of Surveying and Mapping, 2018(2):111-115.
[11]周晓敏,孟晓林,张雪萍,等.倾斜摄影测量的城市真三维模型构建方法[J].测绘科学, 2016, 41(9):159-163.Zhou X M, Meng X L, Zhang X P, et al. A method for urban real3D model building based on oblique photogrammetry[J]. Science of Surveying and Mapping, 2016, 41(9):159-163.
[12]杨国东,王民水.倾斜摄影测量技术应用及展望[J].测绘与空间地理信息, 2016, 39(1):13-15, 18.Yang G D, Wang M S. The tilt photographic measuration technique and expectation[J]. Geomatics&Spatial Information Technology, 2016, 39(1):13-15, 18.
[13]魏占玉, Ramon A,何宏林,等.基于SfM方法的高密度点云数据生成及精度分析[J].地震地质, 2015, 37(2):636-648.Wei Z Y, Ramon A, Heng H L, et al. Accuracy analysis of terrain point cloud acquired by “structure from motion” using aerial photos[J]. Seismology and Geology, 2015, 37(2):636-648.
[14]Lin J, Wang M, Ma M, et al. Aboveground tree biomass estimation of sparse subalpine coniferous forest with UAV oblique photography[J]. Remote Sensing, 2018, 10(11):1849-1868.
[15]陈崇成,李旭,黄洪宇.基于无人机影像匹配点云的苗圃单木冠层三维分割[J].农业机械学报, 2018, 49(2):149-155, 206.Chen C C, Li X, Huang H Y. 3D Segmentation of individual tree canopy in forest nursery based on drone image-matching point cloud[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(2):149-155, 206.
[16]Wallace L, Lucieer A, Malenovsky Z, et al. Assessment of forest structure using two UAV techniques:a comparison of airborne laser scanning and structure from motion(SfM)point clouds[J].Forests, 2016, 7(3):62-78.
[17]Sithole G, Vosselman G. Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2004, 59(1-2):85-101.
[18]Silva C A, Klauberg C, Hentz M K, et al. Comparing the performance of ground filtering algorithms for terrain modeling in a forest environment using airborne LiDAR data[J/OL]. Floresta e Ambiente, 2018, 25(2):e20160150[2018-08-12]. http://dx.doi.org/10.1590/2179-8087.015016.
[19]汪垚,张志玉,倪文俭,等.基于机载LiDAR数据的林下地形提取算法比较与组合分析[J].北京林业大学学报, 2017, 39(12):25-35.Wang Y, Zhang Z Y, Ni W J, et al. Comparison of filter algorithms and combination analysis for DEM extracting based on airborne laser scanning point clouds[J]. Journal of Beijing Forestry University, 2017, 39(12):25-35.
[20]胡永杰,程朋根,陈晓勇,等.机载激光雷达点云滤波算法分析与比较[J].测绘科学技术学报, 2015, 32(1):72-77.Hu Y J, Cheng P G, Chen X Y, et al. The analysis and comparison of airborne LiDAR point cloud filter algorithms[J]. Journal of Geomatics Science and Technology, 2015, 32(1):72-77.
[21]White J C, Tompalski P, Coops N C, et al. Comparison of airborne laser scanning and digital stereo imagery for characterizing forest canopy gaps in coastal temperate rainforests[J]. Remote Sensing of Environment, 2018, 208:1-14.
[22]Zhang W, Qi J, Wan P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing,2016,8(6):501-523.
[23]Axelsson P.DEM generation from laser scanner data using adaptive TIN models[J].International Archives of Photogrammetry and Remote Sensing,2000,33(B4/1,PART4):111-118.
[24]Vosselman G.Slope based filtering of laser altimetry data[J].International Archives of Photogrammetry and Remote Sensing,2000,33(B3/2,PART3):935-942.
[25]Zhang K Q,Chen S Q.A progressive morphological filter for removing nonground measurements from airborne LIDAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2003,4(41):872-882.
[26]段祝庚,肖化顺,袁伟湘.基于离散点云数据的森林冠层高度模型插值方法[J].林业科学,2016,52(9):86-94.Duan Z G,Xiao H S,Yuan W X.Comparison of interpolation methods of forest canopy height model using discrete point cloud data[J].Scientia Silvae Sinicae,2016,52(9):86-94.
[27]王彬,孙虎,徐倩,等.基于无人3D摄影技术的雪松(Cedrus deodara)群落高度测定[J].生态学报,2018,38(10):3524-3533.Wang B,Sun H,Xu Q,et al.Height measurement of a cedar(Cedrus deodara)community based on unmanned aerial vehicles(UAV)3D photogrammetry technology[J].Acta Ecologica Sinica,2018,38(10):3524-3533.
[28]Ni W,Sun G,Pang Y,et al.Mapping three-dimensional structures of forest canopy using UAV stereo imagery:evaluating impacts of forward overlaps and image resolutions with Li DAR data as reference[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(10):3578-3589.
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